Navigating semi-autonomous mobile robots

ABSTRACT

Techniques for navigating semi-autonomous mobile robots are described. A semi-autonomous mobile robot moves within an environment to complete a task. A navigation server communicates with the robot and provides the robot information. The robot includes a navigation map of the environment, interaction information, and a security level. To complete the task, the robot transmits a route reservation request to the navigation server, the route reservation request including a priority for the task, a timeslot, and a route. The navigation server grants the route reservation if the task priority is higher than the task priorities of conflicting route reservation requests from other robots. As the robot moves within the environment, the robot detects an object and attempts to classify the detected object as belonging to an object category. The robot retrieves an interaction profile for the object, and interacts with the object according to the retrieved interaction profile.

TECHNICAL FIELD

The present disclosure relates generally to robotics, and specificallyto navigating semi-autonomous mobile robots.

BACKGROUND

Semi-autonomous mobile robots are appearing in modern life withincreasing frequency. As the use of these robots increases, these robotsare more likely to encounter unexpected objects and situations.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings, which are not necessarily drawn to scale, like numeralsmay describe similar components in different views. Like numerals havingdifferent letter suffixes may represent different instances of similarcomponents. The drawings illustrate generally, by way of example, butnot by way of limitation, various embodiments or examples discussed inthe present document.

FIG. 1 illustrates an example of a system for navigating semi-autonomousmobile robots, according to an embodiment.

FIG. 2 illustrates an example of classifying an object encountered by asemi-autonomous mobile robot within an environment, according to anembodiment.

FIG. 3 illustrates an example of an environment includingsemi-autonomous mobile robots, according to an embodiment.

FIG. 4 illustrates an example of a portion of a navigation map,according to an embodiment.

FIG. 5 illustrates an example of a portion of a redacted navigation map,according to an embodiment.

FIG. 6 illustrates a normal navigation lane configuration of virtualnavigation lanes of an example building corridor during normal operatingconditions, according to an embodiment.

FIG. 7 illustrates an emergency navigation lane configuration of virtualnavigation lanes of an example building corridor during emergencyoperating conditions, according to an embodiment.

FIG. 8 is a flowchart illustrating operations performed during operationof a semi-autonomous mobile robot within an environment, according to anembodiment.

FIG. 9 is a block diagram illustrating an example of a machine, uponwhich any one or more embodiments may be implemented.

DETAILED DESCRIPTION

The present disclosure describes methods, systems, and computer programproducts that individually facilitate navigating semi-autonomous mobilerobots. In this disclosure, the term “robot” refers to a mobileelectro-mechanical machine. A “semi-autonomous” robot is a robot that isunder partial, but not complete, control of an entity external to therobot.

A mobile robot has the capability to move around in its environment andis not fixed to one physical location. An example of a mobile robot thatis in common use today is an automated guided vehicle or automaticguided vehicle (AGV). An AGV is a mobile robot that navigates byfollowing markers or wires in or on the floor, by using computer vision,or by using sensors (e.g., lasers). Mobile robots may be found inindustrial, military, and security environments, among others. They alsoappear as consumer products, for entertainment or to perform certaintasks like vacuum cleaning.

Mobile robots are usually used in tightly controlled environments, suchas on assembly lines, because they have difficulty responding tounexpected interferences. Because of this, most humans rarely encounterrobots outside of industrial, military, or security environments.However, domestic robots, such as those used for cleaning andmaintenance, are increasingly common in and around homes in developedcountries.

Commercial and industrial robots are now in widespread use performingjobs more cheaply and/or with greater accuracy and reliability thanhumans. Mobile robots are also employed for jobs which are too dirty,dangerous, or dull to be suitable for humans. Mobile robots are widelyused in manufacturing, assembly and packing, transport, earth and spaceexploration, surgery, weaponry, laboratory research, and mass productionof consumer and industrial goods.

As robots interact with humans and other robots with increasingfrequency, unique problems may arise. For example, (1) differentiatingbetween humans and non-humans, (2) classifying objects into differentcategories, and (3) prioritization of routes.

FIG. 1 illustrates an example of a system 100 for navigatingsemi-autonomous mobile robots, according to an embodiment. The system100 includes one or more robots 102, a network 106, a navigation server108, and an object profile database 110.

An environment may be as small as a room within a building or may be aslarge as an industrial complex. A robot 102 moves within the environmentbased on factors such as the robot's assigned task(s), objects that mayexist within the environment, physical condition(s) of the environment,etc.

The navigation server 108 and the object profile 110 may be locatedentirely within a closed environment (e.g., a floor of a building, abuilding, a complex of buildings, etc.), may be located partially withinthe closed environment and partially outside of the closed environment,or may be located entirely outside of the closed environment.

A robot 102 communicates with navigation server 108 via network 106. Thenetwork 106 may be a wireless local area network (\WAN), a wide-areanetwork (WAN), or may be the Internet. As a robot 102 operates withinthe environment, the robot 102 transmits various information to thenavigation server 108 and receives various information from thenavigation server 108. For example, a robot 102 may collect variousinformation from its various sensors (e.g., speedometer, accelerometer,camera, motion detector, compass, etc.) and transmit this information tothe navigation server 108. The navigation server 108 may transmitvarious information to the robot 102, such as commands, informationupdates, answers to inquiries submitted by the robot 102, etc.

As a robot 102 operates within the environment, the robot 102 mayencounter various objects. A robot 102 may utilize object detection toattempt to detect whether an object exists within a portion of theenvironment. In an embodiment, the object detection functionality existsentirely within object detection module 112 of robot 102; in anotherembodiment, the object detection functionality exists partially withinobject detection module 112 of robot 102 and partially within anexternal system (e.g., navigation server 108); in yet anotherembodiment, the object detection functionality exists entirely within anexternal system. In embodiments where part or all of the objectdetection functionality exists within an external system, the robot 102transmits information to and receives object detection information fromthe external system via network 106. The object detection functionalitymay use one or more sensors of various types, such as optical sensors,acoustic sensors, infrared sensors, etc. The sensor(s) may be part ofrobot 102, may be external to robot 102, or some combination thereof.

When robot 102 detects an object within an environment, the robot 102may utilize object classification to attempt to classify the detectedobject as belonging to one or more categories. Categories recognized bythe system may be as broad as “living organisms” and “inanimateobjects,” or may be more specific. For example, common categories mayinclude: living creatures, humans, animals, plants, etc.), staticobjects (e.g., immovable objects, such as a wall of a building),static-dynamic objects (e.g., heavy furniture, such as a couch),potentially dynamic objects (e.g., light moveable furniture, such as anoffice chair), and other robots.

In an embodiment, the object classification functionality existsentirely within object classification module 114 of robot 102; inanother embodiment, the object classification functionality existspartially within object classification module 114 of robot 102 andpartially within an external system (e.g., navigation server 108); inyet another embodiment, the object classification functionality existsentirely within an external system. In embodiments where part or all ofthe object classification functionality exists within an externalsystem, the robot 102 transmits information to and receives objectclassification information from the external system via network 106. Theobject classification module 114 may be operable to implement one ormore object classification algorithms or “classifiers,” which may usevarious methods including, but not limited to, machine learningtechniques (e.g., linear classifiers, support vector machines, decisiontrees, neural networks, etc.)

When robot 102 has classified an object within environment as belongingto one or more categories, the robot 102 may utilize objectspecification to attempt to specify the object as a particular objectwithin the category/categories. For example, if object classificationcategorized the object as a human, object specification may identify thehuman as a particular person.

In an embodiment, the object specification functionality exists entirelywithin object specification module 116 of robot 102; in anotherembodiment, the object specification functionality exists partiallywithin object specification module 116 of robot 102 and partially withinan external system (e.g., navigation server 108); in yet anotherembodiment, the object specification functionality exists entirelywithin an external system. In embodiments where part or all of theobject specification functionality exists within an external system, therobot 102 transmits information to and receives object specificationinformation from the external system via network 106. The objectspecification module 116 may be operable to implement one or more objectclassification algorithms or “classifiers,” which may use variousmethods including, but not limited to, machine learning techniques(e.g., linear classifiers, support vector machines, decision trees,neural networks, etc.)

In an embodiment, robot 102 includes navigation information 118, whichmay include one or more navigation maps of: the environment (or portionsof the environment), one or more environments (or portions of theenvironments) adjacent to environment, or some combination thereof.Navigation information 118 may also include one or more routes of robot102 and/or of other robots within the environment. The navigationinformation 118 may be provided to robot 102 by navigation server 108.In an embodiment, navigation server 108 may preplan one or more routesof one or more robots by using timetables and/or synchronized timers.

Navigation information 118 may also include one or more of the followinginformation: (1) information about one or more robots that are inenvironment, were previously in environment, or may be present inenvironment in the future; (2) information regarding past, present, orfuture routes of one or more robots within environment; and (3)information regarding past, present, or future objects withinenvironment.

In an embodiment, robot 102 includes interaction information 120, whichmay include models of behavioral interaction (e.g., profiles) based onobject category and/or identity. Interaction information 120 may includeprofiles for various categories of objects or for specific objects; whenrobot 102 encounters an object 126, the robot 102 may look up theprofile for the object's category (or for the specific object) frominteraction information 120 and proceed to interact with the objectaccording to the object's profile. In an embodiment, navigation server108 includes (or communicates with) object profile database 110, whichstores object category profiles and/or profiles for individual objects.If a robot 102 encounters an object 126 and interaction information 120does not have a profile stored for the object (or for a category, towhich the object belongs), robot 102 may request the missing profilefrom navigation server 108, which may retrieve the profile from objectprofile database 110 and transmit the profile to robot 102. In anembodiment, a robot 102 with a sufficient security level may be able toupdate a profile in the object profile database 110. In an embodiment, aprofile for a person may include one or more of the following: theperson's name, an image of the person's face, an full image of theperson, the person's physical characteristics (e.g., height, weight,voice imprint, etc.); a robot 102 may use one or more of these toidentify a human the robot 102 encounters within the environment.

In an embodiment, interaction information 120 includes priorityinformation for robot 102. The priority information of two or morerobots may be used by navigation server 108 to determine aprioritization of conflicting travel routes of the two or more robots. Aprioritization for a robot 102 may include one or more timeslots and aroute reservation. If a route (or part of a route) includes multiplelanes, a route reservation may include one or more reservations for oneor more lanes. In an embodiment, a robot 102 moving along its route maycontact other robots 102 along the route and inform the other robots 102of its priority; the robots 102 with lower-priority along the route willadjust their routes accordingly so that they do not obstruct the routeof the higher-priority robot 102.

In an embodiment, robot 102 includes security/privacy settings 122.Security settings may involve what information and/or physical areas therobot may access, whereas privacy settings may involve what informationthe robot may share and with whom the robot may share the information.Security settings may include a residency status (e.g., whether therobot 102 is a “resident” or a “guest” within environment), a securitylevel for the robot 102 (e.g., an enumerated level, such as “Secret” or“Top Secret,” or an ordinal number, such as “75”), etc. Privacy settingsmay include a privacy level (e.g., an enumerated level, such as“private,” “public,” or “mixed,” or an ordinal number, such as “10”), adata structure associating information with those entities with whom theinformation may be shared, etc. In an embodiment, the navigation server108 may temporarily elevate the security level of a robot 102 to allowthe robot 102 to perform an emergency task (e.g., rescuing a humantrapped in a portion of the environment that the robot 102 would notnormally be allowed to access). After the emergency has ceased, thenavigation server 108 may command/force the robot 102 to delete allinformation obtained by the robot 102 that is above the robot's 102normal security level, and revert the robot's 102 security level to itspre-emergency level. To facilitate this functionality, in an embodiment,navigation server 108 may require each robot 102 who is to operate inthe environment to have a dedicated memory that may be controlled bynavigation server 108.

In an embodiment, depending upon the interaction information 120 and/orsecurity/privacy settings 122 of a robot 102, robot 102 may communicate124 with another object 126 within the environment. Whether two robots102 may communicate with each other depends upon their respectiveinteraction information 120, security/privacy settings 122, etc.

In an embodiment, the navigation server 108 is able to identify anabnormality in a robot's 102 route or interaction information 120. Forexample, the navigation server 108 may compare the robot's 102 reservedtime slot(s) and reserved route to the time and route actually traversedby the robot 102. If the navigation server 108 identifies anabnormality, the navigation server 108 may mitigate the abnormality inone or more ways. For example, the navigation server 108 may dispatch an“officer” robot to investigate an “abnormal” robot, the navigationserver 108 may alert other robots within the environment of the“abnormal” robot's status, the navigation server 108 may restrict thesecurity level of the “abnormal” robot, the navigation server 108 mayforce the “abnormal” robot to reboot, etc.

FIG. 2 illustrates an example of classifying an object 126 encounteredby a semi-autonomous mobile robot 102 within an environment, accordingto an embodiment. When a robot 102 encounters an object 126 within theenvironment, the robot 102 may attempt to classify the object 126 asbelonging to one or more categories. Common categories may include:“living creatures” (e.g., human 210), “static objects” (e.g., immovableobjects, such as a radio tower 204), “static-dynamic objects” (e.g.,heavy furniture, such as a couch 206), “potentially dynamic objects”(e.g., light moveable furniture, such as an office chair 208), and otherrobots 102,

FIG. 3 illustrates an example of an environment 300 includingsemi-autonomous mobile robots 102A, 102B, according to an embodiment.Although environment 300 is illustrated as multiple rooms within anoffice building, in other embodiments, environment 300 may be a factory,a warehouse, a hospital, a prison, etc.

As each robot 102A, 102B moves within the environment 300, it encountersvarious objects 126 and attempts to classify those objects 126 in to oneor more categories. For example, wall portions 308 are classified as“static objects,” couch 206 is classified as a “static-dynamic object,”office chair 208 is classified as a “potentially dynamic object,” human210 is classified as a “living creature,” and the other robot 102B, 102Ais classified as a robot.

In an embodiment, a robot 102A, 102B may interact differently withobjects belonging to different categories. For example, if a robot 102A,102B moves according to its route and encounters an obstruction, therobot 102A, 102B may interact with the obstruction depending on thecategory of the obstruction: if the obstruction is a wall portion 308 ora couch 206, the robot 102A, 102B will maneuver around the obstruction;if the obstruction is an office chair 208 on casters and without anoccupant, the robot 102A, 102B may move the office chair 208 rather thanmaneuver around the office chair 208.

FIG. 4 illustrates an example of a portion of a navigation map 400,according to an embodiment. The navigation map 400 includes Room A 402,Room B 404, Room X 406, Dining Room 408, and Building Lobby 410. Asillustrated in FIG. 4, the Dining Room 408, as well as corridors 412,414, and 416 are marked, signifying that these areas are high-traffic(e.g., crowded) areas for humans. The navigation map 400 may includeadditional information, such as scheduling information regarding one ormore areas of the navigation map 400. For example, the marked areasillustrated in FIG. 4 may be listed as having heavy traffic on Mondaysthrough Fridays between 11:00 A.M. and 2:00 P.M. Robots 102 may attemptto avoid areas that are marked as high-traffic areas unless no otheroption exists for completion of the robot's 102 task(s).

In an embodiment, a navigation map 400 may include one or more “layers”of information, each layer having one or more privacy configurationswithin security/privacy settings 122. For example, a layer ofinformation with a “public” privacy configuration allows all informationwithin the layer to be shared with the navigation server 108 and/orother robots, a layer of information with a “private” privacyconfiguration denies any information within the layer to be shared withthe navigation server 108 and/or other robots, etc. In an embodiment, alayer of information may have a “mixed” privacy configuration that is inbetween “public” and “private.” For example, a “mixed” privacyconfiguration may allow a malfunctioning robot to alert the navigationserver 108 and/or other robots of its malfunction, but not allow anyother information to be transmitted.

FIG. 5 illustrates an example of a portion of a redacted navigation map500, according to an embodiment. Depending on a robot's 102security/privacy settings 122, the navigation server 108 may provide aredacted version of navigation map 400. For example, thesecurity/privacy settings 122 of a robot 102 do not allow the robot 102to access Room B 404; instead of providing this robot 102 the entirenavigation map 400, navigation server 108 provides this robot 102 aredacted navigation map 500, which does not include informationregarding Room B 404. In an embodiment, a redacted navigation map 500may have the redacted portion(s) of the map marked, such as redactedportion 504.

FIG. 6 illustrates a normal navigation lane configuration 600 of virtualnavigation lanes of an example building corridor during normal operatingconditions, according to an embodiment. The example building corridorincludes external walls 602, 604, and is connected to building exit 614;however, corridors other than those that are connected to building exitsmay also have similarly configured virtual navigation lanes.

A building corridor may have zero or more virtual lanes for humans andone or more virtual lanes for robots. The human and/or robotic lanes maybe “virtual” in the sense that the floor and/or ceiling of the corridormay not be visually marked with lane delineations. Example buildingcorridor is illustrated as having two robotic lanes 606, 608, withrobotic lane 606 directed towards building exit 614 and robotic lane 608directed away from building exit 614. Example building corridor isillustrated as having two human lanes 610, 612, with human lane 610directed towards building exit 614 and human lane 612 directed away frombuilding exit 614.

During normal operating conditions of the building, robots use roboticlane 606 to traverse the corridor towards building exit 614 and userobotic lane 608 to traverse the corridor away from building exit 614;similarly, during normal operating conditions of the building, andhumans use human lane 610 to traverse the corridor towards building exit614 and use human lane 612 to traverse the corridor away from buildingexit 614. In an embodiment, having separate lanes for humans and forrobots reduces collisions between humans and robots. In an embodiment,having two or more opposite direction lanes for humans reducescollisions between humans; similarly for robots.

FIG. 7 illustrates an emergency navigation lane configuration 700 ofvirtual navigation lanes of an example building corridor duringemergency operating conditions, according to an embodiment. In the eventof an emergency requiring occupants of the building to exit thebuilding, navigation server 108 reconfigures the navigation maps of therobots 102 within the building to use emergency navigation laneconfiguration 700 instead of normal navigation lane configuration 600.As illustrated, emergency navigation lane configuration 700 includesunchanged lanes 606, 610 from normal navigation lane configuration 600;however, the direction of robotic lane 608 is reversed to form roboticlane 706, and the direction of human lane 612 is reversed to form humanlane 712. Thus, emergency navigation lane configuration 700 has allvirtual navigation lanes directed towards building exit 614.

FIG. 8 is a flowchart illustrating operations 800 performed duringoperation of a semi-autonomous mobile robot within an environment,according to an embodiment.

A semi-autonomous mobile robot detects an object within the environment(operation 802). The object detection functionality may use one or moresensors of various types, such as optical sensors, acoustic sensors,infrared sensors, etc. The sensor(s) may be part of robot 102, may beexternal to robot 102, or some combination thereof.

Object classification is performed to classify the detected object asbelonging to an object category (operation 804). The objectclassification functionality may be implemented as one or more objectclassification algorithms or “classifiers,” which may use variousmethods including, but not limited to, machine learning techniques(e.g., linear classifiers, support vector machines, decision trees,neural networks, etc.)

Optionally, object specification is performed to specify the classifiedobject as a particular object (operation 806).

An interaction profile for the object is obtained (operation 808).

The semi-autonomous mobile robot interacts with the object according tothe obtained interaction profile (operation 810).

FIG. 9 is a block diagram illustrating an example of a machine 900, uponwhich any one or more embodiments may be implemented. In alternativeembodiments, the machine 900 may operate as a standalone device or maybe connected (e.g., networked) to other machines. In a networkeddeployment, the machine 900 may operate in the capacity of a servermachine, a client machine, or both in a client-server networkenvironment. In an example, the machine 900 may act as a peer machine ina peer-to-peer (P2P) (or other distributed) network environment. Themachine 900 may implement or include any portion of the systems,devices, or methods illustrated in FIGS. 1-8, and may be a sensor, arobot, a server, or any machine capable of executing instructions(sequential or otherwise) that specify actions to be taken by thatmachine. Further, although only a single machine is illustrated, theterm “machine” shall also be taken to include any collection of machinesthat individually or jointly execute a set (or multiple sets) ofinstructions to perform any one or more of the methodologies discussedherein, such as cloud computing, software as a service (SaaS), othercomputer cluster configurations, etc.

Examples, as described herein, may include, or may operate by, logic ora number of components, modules, or mechanisms. Modules are tangibleentities (e.g., hardware) capable of performing specified operations andmay be configured or arranged in a certain manner. In an example,circuits may be arranged (e.g., internally or with respect to externalentities such as other circuits) in a specified manner as a module. Inan example, the whole or part of one or more computer systems (e.g., astandalone, client or server computer system) or one or more hardwareprocessors may be configured by firmware or software (e.g.,instructions, an application portion, or an application) as a modulethat operates to perform specified operations. In an example, thesoftware may reside on a machine-readable medium. In an example, thesoftware, when executed by the underlying hardware of the module, causesthe hardware to perform the specified operations.

Accordingly, the term “module” is understood to encompass a tangibleentity, be that an entity that is physically constructed, specificallyconfigured (e.g., hardwired), or temporarily (e.g., transitorily)configured (e.g., programmed) to operate in a specified manner or toperform part or all of any operation described herein. Consideringexamples in which modules are temporarily configured, each of themodules need not be instantiated at any one moment in time. For example,where the modules comprise a general-purpose hardware processorconfigured using software, the general-purpose hardware processor may beconfigured as respective different modules at different times. Softwaremay accordingly configure a hardware processor, for example, toconstitute a particular module at one instance of time and to constitutea different module at a different instance of time.

Machine (e.g., computer system) 900 may include a hardware processor 902(e.g., a central processing unit (CPU), a graphics processing unit(GPU), a hardware processor core, or any combination thereof), a mainmemory 904 and a static memory 906, some or all of which may communicatewith each other via an interlink (e.g., bus) 908. The machine 900 mayfurther include a display unit 910, an alphanumeric input device 912(e.g., a keyboard), and a user interface (UI) navigation device 914(e.g., a mouse). In an example, the display unit 910, input device 912and UI navigation device 914 may be a touch screen display. The machine900 may additionally include a storage device (e.g., drive unit) 916, asignal generation device 918 (e.g., a speaker), a network interfacedevice 920, and one or more sensors 921, such as a global positioningsystem (GPS) sensor, compass, accelerometer, or other sensor. Themachine 900 may include an output controller 928, such as a serial(e.g., universal serial bus (USB), parallel, or other wired or wireless(e.g., infrared (IR), near field communication (NFC), etc.) connectionto communicate or control one or more peripheral devices (e.g., aprinter, card reader, etc.)

The storage device 916 may include a machine-readable medium 922 onwhich is stored one or more sets of data structures or instructions 924(e.g., software) embodying or utilized by any one or more of thetechniques or functions described herein. The instructions 924 may alsoreside, completely or at least partially, within the main memory 904,within static memory 906, or within the hardware processor 902 duringexecution thereof by the machine 900. In an example, one or anycombination of the hardware processor 902, the main memory 904, thestatic memory 906, or the storage device 916 may constitutemachine-readable media.

Although the machine-readable medium 922 is illustrated as a singlemedium, the term “machine-readable medium” may include a single mediumor multiple media (e.g., a centralized or distributed database, and/orassociated caches and servers) configured to store the one or moreinstructions 924.

The term “machine-readable medium” may include any medium that iscapable of storing, encoding, or carrying instructions for execution bythe machine 900 and that cause the machine 900 to perform any one ormore of the techniques of the present disclosure, or that is capable ofstoring, encoding or carrying data structures used by or associated withsuch instructions. Non-limiting machine-readable medium examples mayinclude solid-state memories, and optical and magnetic media.Accordingly, machine-readable media are not transitory propagatingsignals. Specific examples of machine-readable media may includenon-volatile memory, such as semiconductor memory devices (e.g.,Electrically Programmable Read-Only Memory (EPROM), ElectricallyErasable Programmable Read-Only Memory (EEPROM)) and flash memorydevices; magnetic disks, such as internal hard disks and removabledisks; magneto-optical disks; Random Access Memory (RAM); Solid StateDrives (SSD); and CD-ROM and DVD-ROM disks.

The instructions 924 may further be transmitted or received over acommunications network 926 using a transmission medium via the networkinterface device 920 utilizing any one of a number of transfer protocols(e.g., frame relay, internet protocol (IP), transmission controlprotocol (TCP), user datagram protocol (UDP), hypertext transferprotocol (HTTP), etc.). The term “transmission medium” shall be taken toinclude any intangible medium that is capable of storing, encoding orcarrying instructions for execution by the machine 900, and includesdigital or analog communications signals or other intangible medium tofacilitate communication of such software.

Additional Notes & Example Embodiments

Each of these non-limiting examples can stand on its own, or can becombined in various permutations or combinations with one or more of theother examples.

Example 1 is a system for navigating semi-autonomous mobile robots, thesystem comprising: a navigation server to communicate with asemi-autonomous mobile robot via a network, the robot to move within anenvironment; wherein the semi-autonomous mobile robot comprises:navigation information, including a map of the environment; interactioninformation, including: a model of behavioral interaction between therobot and an object within the environment, the model based on an objectprofile corresponding to the object; and a priority of a task the robotis to complete; and security settings, including a security level of therobot.

In Example 2, the subject matter of Example 1 optionally includes,wherein the navigation server is to: retrieve the object profile from anobject profile database; and transmit the object profile to thesemi-autonomous mobile robot.

In Example 3, the subject matter of any one or more of Examples 1-2optionally include, wherein the semi-autonomous mobile robot furthercomprises: an object detection module to detect an object within theenvironment; an object classification module to classify a detectedobject as a member of an object category from a plurality of objectcategories; and an object specification module to specify a classifiedobject as a particular object within the object category.

In Example 4, the subject matter of Example 3 optionally includes,wherein the object profile corresponds to the object category.

In Example 5, the subject matter of any one or more of Examples 3-4optionally include, wherein the object profile corresponds to theparticular object within the object category.

In Example 6, the subject matter of any one or more of Examples 3-5optionally include, wherein the plurality of object categoriescomprises: living creatures; static objects; static-dynamic objects;potentially dynamic objects; and robots.

In Example 7, the subject matter of any one or more of Examples 1-6optionally include, wherein the semi-autonomous mobile robot is torequest a route reservation from the navigation server, the routereservation request comprising: the priority of the task the robot is tocomplete; a slot of time; and a route within the environment during theslot of time.

In Example 8, the subject matter of Example 7 optionally includes,wherein the navigation server is to grant the route reservation requestbased at least partially on the priority of the task the robot is tocomplete.

In Example 9, the subject matter of any one or more of Examples 1-8optionally include, wherein the security level is an ordinal number.

In Example 10, the subject matter of any one or more of Examples 1-9optionally include, wherein the map of the environment is redacted basedat least partially on the security level of the robot.

In Example 11, the subject matter of any one or more of Examples 1-10optionally include, wherein the navigation information furthercomprises: information regarding a plurality of objects within theenvironment.

In Example 12, the subject matter of any one or more of Examples 1-11optionally include, wherein the environment is at least one of: afactory; a hospital; an office building; and a prison.

In Example 13, the subject matter of any one or more of Examples 1-12optionally include, wherein the navigation server is to: receiveinformation collected by the semi-autonomous mobile robot; and transmitinformation to the semi-autonomous mobile robot.

In Example 14, the subject matter of any one or more of Examples 1-13optionally include, wherein the semi-autonomous robot is one of aplurality of semi-autonomous robots to operate within the environment.

In Example 15, the subject matter of any one or more of Examples 1-14optionally include, wherein the interaction information includes aprivacy setting specifying information the semi-autonomous mobile robotmay share with the object.

Example 16 is a method for navigating semi-autonomous mobile robots, themethod comprising: communicating, by a navigation server with asemi-autonomous mobile robot via a network, the robot to move within anenvironment; wherein the semi-autonomous mobile robot comprises:navigation information, including a map of the environment; interactioninformation, including: a model of behavioral interaction between therobot and an object within the environment, the model based on an objectprofile corresponding to the object; and a priority of a task the robotis to complete; and security settings, including a security level of therobot.

In Example 17, the subject matter of Example 16 optionally includes:retrieving, by the navigation server, the object profile from an objectprofile database; and transmitting, from the navigation server, theobject profile to the semi-autonomous mobile robot.

In Example 18, the subject matter of any one or more of Examples 16-17optionally include, wherein the semi-autonomous mobile robot furthercomprises: an object detection module to detect an object within theenvironment; an object classification module to classify a detectedobject as a member of an object category from a plurality of objectcategories; and an object specification module to specify a classifiedobject as a particular object within the object category.

In Example 19, the subject matter of Example 18 optionally includes,wherein the object profile corresponds to the object category.

In Example 20, the subject matter of any one or more of Examples 18-19optionally include, wherein the object profile corresponds to theparticular object within the object category.

In Example 21, the subject matter of any one or more of Examples 18-20optionally include, wherein the plurality of object categoriescomprises: living creatures; static objects; static-dynamic objects;potentially dynamic objects; and robots.

In Example 22, the subject matter of any one or more of Examples 16-21optionally include, wherein the semi-autonomous mobile robot is torequest a route reservation from the navigation server, the routereservation request comprising: the priority of the task the robot is tocomplete; a slot of time; and a route within the environment during theslot of time.

In Example 23, the subject matter of Example 22 optionally includes:granting, by the navigation server, the route reservation request basedat least partially on the priority of the task the robot is to complete.

In Example 24, the subject matter of any one or more of Examples 16-23optionally include, wherein the security level is an ordinal number.

In Example 25, the subject matter of any one or more of Examples 16-24optionally include, wherein the map of the environment is redacted basedat least partially on the security level of the robot.

In Example 26, the subject matter of any one or more of Examples 16-25optionally include, wherein the navigation information furthercomprises: information regarding a plurality of objects within theenvironment.

In Example 27, the subject matter of any one or more of Examples 16-26optionally include, wherein the environment is at least one of: afactory; a hospital; an office building; and a prison.

In Example 28, the subject matter of any one or more of Examples 16-27optionally include, further comprising: receiving, by the navigationserver, information collected by the semi-autonomous mobile robot; andtransmitting, from the navigation server, information to thesemi-autonomous mobile robot.

In Example 29, the subject matter of any one or more of Examples 16-28optionally include, wherein the semi-autonomous robot is one of aplurality of semi-autonomous robots operating within the environment.

In Example 30, the subject matter of any one or more of Examples 16-29optionally include, wherein the interaction information includes aprivacy setting specifying information the semi-autonomous mobile robotmay share with the object.

Example 31 is at least one machine-readable medium includinginstructions which, when executed by a machine, cause the machine toperform operations of any of the methods of Examples 16-29.

Example 32 is an apparatus comprising means for performing any of themethods of Examples 16-29.

Example 33 is an apparatus for navigating semi-autonomous mobile robots,the apparatus comprising: means for communicating, by a navigationserver with a semi-autonomous mobile robot via a network, the robot tomove within an environment; wherein the semi-autonomous mobile robotcomprises: navigation information, including a map of the environment;interaction information, including: a model of behavioral interactionbetween the robot and an object within the environment, the model basedon an object profile corresponding to the object; and a priority of atask the robot is to complete; and security settings, including asecurity level of the robot.

In Example 34, the subject matter of Example 33 optionally includes:means for retrieving, by the navigation server, the object profile froman object profile database; and means for transmitting, from thenavigation server, the object profile to the semi-autonomous mobilerobot.

In Example 35, the subject matter of any one or more of Examples 33-34optionally include, wherein the semi-autonomous mobile robot furthercomprises: an object detection module to detect an object within theenvironment; an object classification module to classify a detectedobject as a member of an object category from a plurality of objectcategories; and an object specification module to specify a classifiedobject as a particular object within the object category.

In Example 36, the subject matter of Example 35 optionally includes,wherein the object profile corresponds to the object category.

In Example 37, the subject matter of any one or more of Examples 35-36optionally include, wherein the object profile corresponds to theparticular object within the object category.

In Example 38, the subject matter of any one or more of Examples 35-37optionally include, wherein the plurality of object categoriescomprises: living creatures; static objects; static-dynamic objects;potentially dynamic objects; and robots.

In Example 39, the subject matter of any one or more of Examples 33-38optionally include, wherein the semi-autonomous mobile robot is torequest a route reservation from the navigation server, the routereservation request comprising: the priority of the task the robot is tocomplete; a slot of time; and a route within the environment during theslot of time.

In Example 40, the subject matter of Example 39 optionally includes:means for granting, by the navigation server, the route reservationrequest based at least partially on the priority of the task the robotis to complete.

In Example 41, the subject matter of any one or more of Examples 33-40optionally include, wherein the security level is an ordinal number.

In Example 42, the subject matter of any one or more of Examples 33-41optionally include, wherein the map of the environment is redacted basedat least partially on the security level of the robot.

In Example 43, the subject matter of any one or more of Examples 33-42optionally include, wherein the navigation information furthercomprises: information regarding a plurality of objects within theenvironment.

In Example 44, the subject matter of any one or more of Examples 33-43optionally include, wherein the environment is at least one of: afactory; a hospital; an office building; and a prison.

In Example 45, the subject matter of any one or more of Examples 33-44optionally include, further comprising: means for receiving, by thenavigation server, information collected by the semi-autonomous mobilerobot; and means for transmitting, from the navigation server,information to the semi-autonomous mobile robot.

In Example 46, the subject matter of any one or more of Examples 33-45optionally include, wherein the semi-autonomous robot is one of aplurality of semi-autonomous robots operating within the environment.

In Example 47, the subject matter of any one or more of Examples 33-46optionally include, wherein the interaction information includes aprivacy setting specifying information the semi-autonomous mobile robotmay share with the object.

Conventional terms in the fields of computer networking and computersystems have been used herein. The terms are known in the art and areprovided only as a non-limiting example for convenience purposes.Accordingly, the interpretation of the corresponding terms in theclaims, unless stated otherwise, is not limited to any particulardefinition.

Although specific embodiments have been illustrated and describedherein, it will be appreciated by those of ordinary skill in the artthat any arrangement that is calculated to achieve the same purpose maybe substituted for the specific embodiments shown. Many adaptations willbe apparent to those of ordinary skill in the art. Accordingly, thisapplication is intended to cover any, adaptations or variations.

The above detailed description includes references to the accompanyingdrawings, which form a part of the detailed description. The drawingsshow, by way of illustration, specific embodiments that may bepracticed. These embodiments are also referred to herein as “examples.”Such examples may include elements in addition to those shown ordescribed. However, the present inventors also contemplate examples inwhich only those elements shown or described are provided. Moreover, thepresent inventors also contemplate examples using any combination orpermutation of those elements shown or described (or one or more aspectsthereof), either with respect to a particular example (or one or moreaspects thereon, or with respect to other examples (or one or moreaspects thereof) shown or described herein.

In this document, the terms “a” or “an” are used, as is common in patentdocuments, to include one or more than one, independent of any otherinstances or usages of “at least one” or “one or more.” In thisdocument, the term “or” is used to refer to a nonexclusive or, such that“A or B” includes “A but not B,” “B but not A,” and “A and B,” unlessotherwise indicated. Moreover, in the following claims, the terms“first,” “second,” and “third,” etc. are used merely as labels, and arenot intended to impose numerical requirements on their objects.

In this Detailed Description, various features may have been groupedtogether to streamline the disclosure. This should not be interpreted asintending that an unclaimed disclosed feature is essential to any claim.Rather, inventive subject matter may lie in less than all features of aparticular disclosed embodiment Thus, the following claims are herebyincorporated into the Detailed Description, with each claim standing onits own as a separate embodiment, and it is contemplated that suchembodiments may be combined with each other in various combinations orpermutations. The scope of the embodiments should be determined withreference to the appended claims, along with the full scope ofequivalents to which such claims are entitled.

The above description is intended to be illustrative, and notrestrictive. For example, the above-described examples (or one or moreaspects thereof) may be used in combination with each other. Otherembodiments may be used, such as by one of ordinary skill in the artupon reviewing the above description.

1.-20. (canceled)
 21. A robot comprising: a body; a motor to move thebody in an environment; wireless communication circuitry; at least onesensor carried by the body, the at least one sensor to output at leastone signal corresponding to an object in the environment; a storagedevice carried by the body; and at least one processor carried by thebody, the at least one processor to execute instructions to: classifythe object based on the at least one signal from the at least one sensorand object profile data, determine a first travel path of the robotbased on the classification of the object, a map of the environment andsecurity settings, the security settings to identify an area the robotis forbidden to enter, the at least one processor to determine the firsttravel path to avoid the area, and change the first travel path to asecond travel path in response to a notification of an emergency. 22.The robot as defined in claim 21, wherein the at least one processor isto determine the first travel path of the robot to avoid an area havingtraffic.
 23. The robot as defined in claim 21, wherein the at least oneprocessor is to classify the object in a first object classification orin a second object classification, and wherein the first objectclassification corresponds to humans and the second objectclassification corresponds to non-human objects.
 24. The robot asdefined in claim 23, wherein the second object classificationcorresponds to robots.
 25. The robot as defined in claim 21, wherein theat least one processor is to set an emergency level of the robot inresponse to the notification of the emergency.
 26. The robot as definedin claim 21, wherein the first travel path includes a first laneassigned to robots, the first lane separate from a second lane assignedto humans.
 27. The robot as defined in claim 26, wherein the at leastone processor is to vary at least one of the first or second lanes inresponse to the notification of the emergency.
 28. The robot as definedin claim 27, wherein the at least one processor is to is to change adirection of the at least one of the first or second lanes in responseto the notification of the emergency.
 29. The robot as defined in claim27, wherein the at least one processor is to change an assignment of thefirst lane to humans in response to the notification of the emergency.30. The robot as defined in claim 21, wherein the second travel pathenters the area the robot is forbidden to enter.
 31. A storage devicecomprising instructions which, when executed, cause at least oneprocessor of a robot to: classify an object based on object profile dataand at least one signal from at least one sensor; determine a firsttravel path of the robot based on the classification of the object, amap of an environment and security settings, the security settings toidentify an area the robot is forbidden to enter, the first travel pathto avoid the area; and change the first travel path to a second travelpath in response to a notification of an emergency.
 32. The storagedevice as defined in claim 31, wherein the area is a first area, andwherein the instructions cause the at least one processor to determinethe first travel path of the robot to avoid a second area having trafficcorresponding to humans.
 33. The storage device as defined in claim 31,wherein the instructions further cause the at least one processor toclassify the object in a first object classification or in a secondobject classification, and wherein the first object classificationcorresponds to humans and the second object classification correspondsto non-human objects.
 34. The storage device as defined in claim 33,wherein the second object classification corresponds to robots.
 35. Thestorage device as defined in claim 31, wherein the instructions causethe at least one processor to set to an emergency level of the robot inresponse to the notification of the emergency.
 36. The storage device asdefined in claim 31, wherein the instructions cause the at least oneprocessor to change a first lane or a second lane in response to thenotification of the emergency, the first lane assigned to robots and thesecond lane assigned to humans.
 37. The storage device as defined inclaim 36, wherein the instructions cause the at least one processor tochange a direction of the first lane in response to the notification ofthe emergency.
 38. The storage device as defined in claim 36, whereinthe instructions cause the at least one processor to change theassignment of the at least one of the first lane in response to thenotification of the emergency.
 39. An apparatus comprising: means foroutputting at least one signal corresponding to an object in anenvironment; means for classifying the object based on the at least onesignal and object profile data; and means for processing to: determine afirst travel path of a robot based on the classification of the object,a map of the environment and security settings that identify an area therobot is forbidden to enter, the first travel path to be determined toavoid the area, and change the first travel path to a second travel pathin response to a notification of an emergency.
 40. The apparatus asdefined in claim 39, wherein the processing means is to assign lanes forhuman use and robot use.
 41. The apparatus as defined in claim 39,wherein the classifying means is to determine whether an object is ahuman or a non-human object.
 42. The apparatus as defined in claim 39,wherein the processing means is to determine the first travel path toavoid an area having traffic.